Data Visualization with Python and Matplotlib Training Course

In this course, you will learn how to present data in an explicit manner using the Python as a Data Visualization Tool. This course will help you learn how to arrange critical and meaningful data in a simple yet sophisticated way that is required for decision making.

Subscribe to this course +

USD 50

Subscribe Now

Group Training

Looking for a personalized training for a group (3 or more participants) at your preferred location?
Contact us

Course Overview

Data visualization means representing the data in a visual format. Visual formats include graphs, charts, and pictograms. It's of great advantage to learn to deploy data visualization through Python using Matplotlib. Matplotlib is a library of Python that helps in the viewing of the data. You will learn how to deploy various commands for creating multiple graphs, 3D scatter diagrams, and Pie Charts among others. In this course, you will also learn about actual geographical plotting using Matplotlib extension called Basemap.

1-year access to audio-video lectures
Course completion certificate

Course Outline

Skills

This course helps you visualize varied forms of 2D and 3D graphs, like bar charts, line graphs, scatter plots, etc. You will also learn how to customize those graphs such as modifying lines, colors, etc.

Alignment

This course is aligned to help you learn various ways to visually present python data

Outcome

On finishing this course, you will gain a complete understanding of the options available for visualizing data and know how to create well presented, visually appealing graphs.

Course Introduction

Introduction

Getting Matplotlib And Setting Up
Different types of basic Matplotlib charts

Section Introduction

Basic matplotlib graph

Labels, titles and window buttons

Legends

Bar Charts

Histograms

Scatter Plots

Stack Plots

Pie Chart

Loading data from a CSV

Loading data with NumPy

Section Conclusion
Basic Customization Options

Section Introduction

Source for our Data*

Parsing stock prices from the internet*

Plotting basic stock data*

Modifying labels and adding a grid*

Converting from unix time and adjusting subplots*

Customizing ticks*

Fills and Alpha*

Add, remove, and customize spines*

Candlestick OHLC charts*

Styles with Matplotlib*

Creating our own Style*

Live Graphs*

Adding and placing text*

Annotating a specific plot*

Dynamic annotation of last price*

Section Conclusion
Advanced Customization Options

Section Introduction

Basic suplot additions*

Subplot2grid *

Incorporating changes to candlestick graph*

Creating moving averages with our data*

Adding a High minus Low indicator to graph*

Customizing the dates that show*

Label and Tick customizations*

Share X axis*

Multi Y axis*

Customizing Legends*

Section Conclusion

Geographical Plotting with Basemap

Section Introduction

Downloading and installing Basemap

Basic basemap example

Customizing the projection

More customization, like colors, fills, and forms of boundaries

Plotting Coordinates*

Connecting Coordinates*

Section Conclusion
3D graphing

Section Introduction

Basic 3D graph example using wire_frame

3D scatter plots

3D Bar Charts

More advanced Wireframe example

Section Conclusion
Course Conclusion

Conclusion